Debugging with dominance: on-the-fly RTL debug solution implications
Bibliographic record
Abstract
Design debugging has become a resource-intensive bottleneck in modern VLSI CAD flows, consuming as much as 60% of the total verification effort. With typical design sizes exceeding the half-million synthesized gates mark, the growing number of blocks to be examined dramatically slows down the debugging process. The aim of this work is to prune the number of debugging iterations for finding all potential bugs, without affecting the debugging resolution. This is achieved by using structural dominance relationships between circuit components. More specifically, an iterative fixpoint algorithm is presented for finding dominance relationships between multiple-output blocks of the design. These relationships are then leveraged for the early discovery of potential bugs, along with their corrections, resulting in significant debugging speed-ups. Extensive experiments on real industrial designs show that 66 % of solutions are discovered early due to dominator implications. This results in consistent performance gains in all cases and a 1.7x overall speed-up for finding all potential bugs, demonstrating the robustness and practicality of the proposed approach. 1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".